Discovering the governing equations of nature is key to many scientific disciplines. Many complex systems can be described by mathematical equations, ranging from Hooke's law for harmonic oscillators to Maxwell's equations for electrodynamics. While scientists have often spent years developing insights to discover these equations, machine learning has become alluring in its potential to tackle and automate extremely complex tasks. For example, deep learning in recent years has been able to create images from captions [1]} and predict a protein's 3D structure [2]} better and faster than humans. However, deep learning models are often black-box, making it difficult to gain scientific insight from these techniques. Thus, in order to make deep learning widely applicable for scientific discovery, we covet methods that are interpretable so that scientists can extract meaningful information from complex datasets.
